Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. If not placed correctly, they can cause serious harm, even death to patients. Recent AI developments demonstrate the feasibility of robustly detecting NGT placement from Chest X-ray images to reduce risks of sub-optimally or critically placed NGTs being missed or delayed in their detection, but gaps remain in clinical practice integration. In this study, we present a human-centered approach to the problem and describe insights derived following contextual inquiry and in-depth interviews with 15 clinical stakeholders. The interviews helped understand challenges in existing workflows, and how best to align technical capabilities with user needs and expectations. We discovered the tradeoffs and complexities that need consideration when choosing suitable workflow stages, target users, and design configurations for different AI proposals. We explored how to balance AI benefits and risks for healthcare staff and patients within broader organizational, technical, and medical-legal constraints. We also identified data issues related to edge cases and data biases that affect model training and evaluation; how data documentation practices influence data preparation and labeling; and how to measure relevant AI outcomes reliably in future evaluations. We discuss how our work informs design and development of AI applications that are clinically useful, ethical, and acceptable in real-world healthcare services.

Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare applications. Focusing on the domain of radiology, vision-language models (VLMs) achieve good performance results for tasks such as generating radiology findings based on a patient’s medical image, or answering visual questions (e.g., “Where are the nodules in this chest X-ray?”). However, the clinical utility of potential applications of these capabilities is currently underexplored. We engaged in an iterative, multidisciplinary design process to envision clinically relevant VLM interactions, and co-designed four VLM use concepts: Draft Report Generation, Augmented Report Review, Visual Search and Querying, and Patient Imaging History Highlights. We studied these concepts with 13 radiologists and clinicians who assessed the VLM concepts as valuable, yet articulated many design considerations. Reflecting on our findings, we discuss implications for integrating VLM capabilities in radiology, and for healthcare AI more generally.

Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data.

Qianchu Liu, Stephanie Hyland, Shruthi Bannur, Kenza Bouzid, Daniel Castro, Maria Wetscherek, Robert Tinn, Harshita Sharma, Fernando Pérez-García, Anton Schwaighofer, Pranav Rajpurkar, Sameer Khanna, Hoifung Poon, Naoto Usuyama, Anja Thieme, Aditya Nori, Matthew Lungren, Ozan Oktay, Javier Alvarez-Valle. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

In sponsored search auctions, the auctioneer operates the marketplace by setting a number of auction parameters such as reserve prices for the task of auction optimization. The auction parameters may be set for each individual keyword, but the optimization problem becomes intractable since the number of keywords is in the millions. To reduce the dimensionality and generalize well, one wishes to cluster keywords or queries into meaningful groups, and set parameters at the keyword-cluster level. For auction optimization, keywords shall be deemed as interchangeable commodities with respect to their valuations from advertisers, represented as bid distributions or landscapes. Clustering keywords for auction optimization shall thus be based on their bid distributions. In this paper we present a formalism of clustering probability distributions, and its application to query clustering where each query is represented as a probability density of click-through rate (CTR) weighted bid and distortion is measured by KL divergence. We first derive a k-means variant for clustering Gaussian densities, which have a closed-form KL divergence. We then develop an algorithm for clustering Gaussian mixture densities, which generalize a single Gaussian and are typically a more realistic parametric assumption for real-world data. The KL divergence between Gaussian mixture densities is no longer analytically tractable; hence we derive a variational EM algorithm that minimizes an upper bound of the total within-cluster KL divergence. The clustering algorithm has been deployed successfully into production, yielding significant improvement in revenue and clicks over the existing production system. While motivated by the specific setting of query clustering, the proposed clustering method is generally applicable to many real-world applications where an example is better characterized by a distribution than a finite-dimensional feature vector in Euclidean space as in the classical k-means.

We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equivalence between parametric linear models and nonparametric Gaussian processes (GPs). The resulting models can be learned easily via an EM-algorithm. Empirical studies on multi-label text categorization suggest that the presented models allow accurate solutions of these multi-task problems.

We present a novel method for learning with Gaussian process regres- sion in a hierarchical Bayesian framework. In a first step, kernel matri- ces on a fixed set of input points are learned from data using a simple and efficient EM algorithm. This step is nonparametric, in that it does not require a parametric form of covariance function. In a second step, kernel functions are fitted to approximate the learned covariance matrix using a generalized Nystrom method, which results in a complex, data driven kernel. We evaluate our approach as a recommendation engine for art images, where the proposed hierarchical Bayesian method leads to excellent prediction performance. 1 Introduction In many real-world application domains, the available training data sets are quite small, which makes learning and model selection difficult. For example, in the user preference modelling problem we will consider later, learning a preference model would amount to fitting a model based on only 20 samples of a user's preference data. Fortunately, there are situations where individual data sets are small, but data from similar scenarios can be obtained. Returning to the example of preference modelling, data for many different users are typically available. This data stems from clearly separate individuals, but we can expect that models can borrow strength from data of users with similar tastes. Typically, such problems have been handled by either mixed effects models or hierarchical Bayesian modelling. In this paper we present a novel approach to hierarchical Bayesian modelling in the context of Gaussian process regression, with an application to recommender systems. Here, hier- archical Bayesian modelling essentially means to learn the mean and covariance function of the Gaussian process. In a first step, a common collaborative kernel matrix is learned from the data via a simple and efficient EM algorithm. This circumvents the problem of kernel design, as no paramet- ric form of kernel function is required here. Thus, this form of learning a covariance matrix is also suited for problems with complex covariance structure (e.g. nonstationarity). A portion of the learned covariance matrix can be explained by the input features and, thus, generalized to new objects via a content-based kernel smoother. Thus, in a second step, we generalize the covariance matrix (learned by the EM-algorithm) to new items using a generalized Nystrom method. The result is a complex content-based kernel which itself is a weighted superposition of simple smoothing kernels. This second part could also be applied to other situations where one needs to extrapolate a covariance matrix on a finite set (e.g. a graph) to a continuous input space, as, for example, required in induction for semi-supervised learning [14]. The paper is organized as follows. Sec. 2 casts Gaussian process regression in a hierarchical Bayesian framework, and shows the EM updates to learn the covariance matrix in the first step. Extrapolating the covariance matrix is shown in Sec. 3. We illustrate the function of the EM-learning on a toy example in Sec. 4, before applying the proposed methods as a recommender system for images in Sec. 4.1. 1.1 Previous Work In statistics, modelling data from related scenarios is typically done via mixed effects mod- els or hierarchical Bayesian (HB) modelling [6]. In HB, parameters of models for indi- vidual scenarios (e.g. users in recommender systems) are assumed to be drawn from a common (hyper)prior distribution, allowing the individual models to interact and regular- ize each other. Recent examples of HB modelling in machine learning include [1, 2]. In other contexts, this learning framework is called multi-task learning [4]. Multi-task learn- ing with Gaussian processes has been suggested by [8], yet with the rather stringent as- sumption that one has observations on the same set of points in each individual scenario. Based on sparse approximations of GPs, a more general GP multi-task learner with para- metric covariance functions has been presented in [7]. In contrast, the approach presented in this paper only considers covariance matrices (and is thus non-parametric) in the first step. Only in a second extrapolation step, kernel smoothing leads to predictions based on a covariance function that is a data-driven combination of simple kernel functions. 2 Learning GP Kernel Matrices via EM The learning task we are concerned with can be stated as follows: The data are observations from M different scenarios. In the i.th scenario, we have observations yi = (yi , . . . , yi ) 1 N i on a total of N i points, Xi = {xi , . . . , xi } 1 . In order to analyze this data in a hierarchical N i Bayesian way, we assume that the data for each scenario is a noisy sample of a Gaussian process (GP) with unknown mean and covariance function. We assume that mean and covariance function are shared across different scenarios.1 In the first modelling step presented in this section, we consider transductive learning ("la- belling a partially labelled data set"), that is, we are interested in the model's behavior only on points X, with X = M Xi and cardinality N = |X|. This situation is relevant i=1 for most collaborative filtering applications. Thus, test points are the unlabelled points in each scenario. This reduces the whole "infinite dimensional" Gaussian process to its finite dimensional projection on points X, which is an N -variate Gaussian distribution with co- variance matrix K and mean vector m. For the EM algorithm to work, we also require that there is some overlap between scenarios, that is, Xi Xj = for some i, j. Coming back to the user modelling problem mentioned above, this means that at least some items have been rated by more than one user. Thus, our first modelling step focusses on directly learning the covariance matrix K and 1Alternative HB approaches for collaborative filtering, like that discussed in [5], assume that model weights are drawn from a shared Gaussian distribution. m from the data via an efficient EM algorithm. This may be of particular help in problems where one would need to specify a complex (e.g. nonstationary) covariance function. Following the hierarchical Bayesian assumption, the data observed in each scenario is thus a partial sample from N (y | m, K + 21), where 1 denotes the unit matrix. The joint model is simply M p(m, K) p(yi | f i)p(f i | m, K), (1) i=1 where p(m, K) denotes the prior distribution for mean and covariance. We assume a Gaus- sian likelihood p(yi | f i) with diagonal covariance matrix 21. 2.1 EM Learning For the above hierarchical Bayesian model, Eq. (1), the marginal likelihood becomes M p(m, K) p(yi | f i)p(f i | m, K) df i. (2) i=1 To obtain simple and stable solutions when estimating m and K from the data, we con- sider point estimates of the parameters m and K, based on a penalized likelihood approach with conjugate priors.2 The conjugate prior for mean m and covariance K of a multivari- ate Gaussian is the so-called Normal-Wishart distribution [6], which decomposes into the product of an inverse Wishart distribution for K and a Normal distribution for m, p(m, K) = N (m | , -1K)Wi-1(K|, U ). (3) That is, the prior for the Gram matrix K is given by an inverse Wishart distribution with scalar parameter > 1/2(N - 1) and U being a symmetric positive-definite matrix. Given the covariance matrix K, m is Gaussian distributed with mean and covariance -1K, where is a positive scalar. The parameters can be interpreted in terms of an equivalent data set for the mean (this data set has size A, with A = , and mean = ) and a data set for the covariance that has size B, with = (B + N )/2, and covariance S, U = (B/2)S. In order to write down the EM algorithm in a compact way, we denote by I(i) the set of indices of those data points that have been observed in the i.th scenario, that is I(i) = {j | j {1, . . . , N } and xj Xi}. Keep in mind that in most applications of interest N i N such that most targets are missing in training. KI(i),I(i) denotes the square submatrix of K that corresponds to points I(i), that is, the covariance matrix for points in the i.th scenario. By K,I(i) we denote the covariance matrix of all N points versus those in the i.th scenario.

Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper, we develop a probabilistic framework for memory-based CF (PMCF). While this framework has clear links with classical memory-based CF, it allows us to find principled solutions to known problems of CF-based recommender systems. In particular, we show that a probabilistic active learning method can be used to actively query the user, thereby solving the "new user problem." Furthermore, the probabilistic framework allows us to reduce the computational cost of memory-based CF by working on a carefully selected subset of user profiles, while retaining high accuracy. We report experimental results based on two real-world data sets, which demonstrate that our proposed PMCF framework allows an accurate and efficient prediction of user preferences.

In this article, we present a novel approach to solving the localization problem in cellular networks. The goal is to estimate a mobile user’s position, based on measurements of the signal strengths received from network base stations. Our solution works by building Gaussian process models for the distribution of signal strengths, as obtained in a series of calibration measurements. In the localization stage, the user’s posi- tion can be estimated by maximizing the likelihood of received signal strengths with respect to the position. We investigate the accuracy of the proposed approach on data obtained within a large indoor cellular network.

The application range of memory-based collaborative filtering (CF) is limited due to CF's high memory consumption and long runtime. The approach presented in this paper removes redundant and inconsistent instances (users) from the data. This paper aims to distinguish informative instances (users) from large raw user preference database and thus alleviate the memory and runtime cost of the widely used memory-based collaborative filtering (CF) algorithm. Our work shows that a satisfactory accuracy can be achieved by using only a small portion of the original data set, thereby alleviating the storage and runtime cost of the CF algorithm. In our approach, we consider instance selection as the problem of selecting informative data that increase the We begin by discussing the instance selection problem in a general sense that is to increase a posteriori probability of the optimal model by selecting informative data. We evaluate the empirical performance of our approach PF on two real-world data sets and attain very promisingpositive experimental results. The dData size and the prediction time are significantly reduced, while the prediction accuracy is on a par with almost the same as the results achieved by using the complete database.

Gaussian process regression allows a simple analytical treatment of ex- act Bayesian inference and has been found to provide good performance, yet scales badly with the number of training data. In this paper we com- pare several approaches towards scaling Gaussian processes regression to large data sets: the subset of representers method, the reduced rank approximation, online Gaussian processes, and the Bayesian commit- tee machine. Furthermore we provide theoretical insight into some of our experimental results. We found that subset of representers methods can give good and particularly fast predictions for data sets with high and medium noise levels. On complex low noise data sets, the Bayesian committee machine achieves significantly better accuracy, yet at a higher computational cost.

We describe the RA scanner, a novel system for the examination of pa- tients suffering from rheumatoid arthritis. The RA scanner is based on a novel laser-based imaging technique which is sensitive to the optical characteristics of finger joint tissue. Based on the laser images, finger joints are classified according to whether the inflammatory status has improved or worsened. To perform the classification task, various lin- ear and kernel-based systems were implemented and their performances were compared. Special emphasis was put on measures to reliably per- form parameter tuning and evaluation, since only a very small data set was available. Based on the results presented in this paper, it was con- cluded that the RA scanner permits a reliable classification of patholog- ical finger joints, thus paving the way for a further development from prototype to product stage.